让AI自动构建可纠错的动态任务网络，随对话逐步演化。
IACT: A Self-Organizing Recursive Model for General AI Agents: A Technical White Paper on the Architecture Behind kragent.ai
- 用双向对话替代固定调用，动态生成递归任务结构。
- 通过交互冗余机制实时修正错误，解决歧义问题。
- 适合复杂开放任务，无需预设流程，适合生产级部署。
本文介绍交互式代理调用树（IACT），一种旨在克服静态硬编码工作流局限性的计算模型。与传统需预先定义图结构或特殊编程的系统不同，IACT作为通用自主系统，仅通过用户对话驱动。给定高层目标后，系统会增量式地自组织出动态、递归的代理拓扑，以匹配任务的复杂性结构。为缓解单向函数调用中的误差传播问题，IACT引入交互冗余机制，将刚性调用替换为双向、有状态的对话，从而实现运行时错误纠正与歧义消解。本文描述了该模型在kragent.ai系统中的架构设计、核心原则及实际部署经验，基于真实工作流的定性证据，而非详尽基准测试。
原文摘要 · Abstract (English)
This technical white paper introduces the Interactive Agents Call Tree (IACT), a computational model designed to address the limitations of static, hard-coded agent workflows. Unlike traditional systems that require pre-defined graphs or specialized programming, IACT operates as a general-purpose autonomous system driven purely by user dialogue. Given a high-level objective, the system autonomously grows a dynamic, recursive agent topology incrementally tailored to the problem's structure. This allows it to scale its organizational complexity to match open-ended tasks. To mitigate the error propagation inherent in unidirectional function calls, IACT introduces interactional redundancy by replacing rigid invocations with bidirectional, stateful dialogues. This mechanism enables runtime error correction and ambiguity resolution. We describe the architecture, design principles, and practical lessons behind the production deployment of this model in the kragent.ai system, presenting qualitative evidence from real-world workflows rather than exhaustive benchmark results.
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